Code
import pandas as pd
df= pd.read_csv("data/eda_data.csv")import pandas as pd
df= pd.read_csv("data/eda_data.csv")import pandas as pd
skills_data = {
"Name": ["Binderiya", "Pranjul", "Pratham", "Panyang"],
"Python": [4, 4, 5, 3],
"SQL": [4, 4, 5, 4],
"Machine Learning": [2, 3, 2, 2],
"PySpark": [3, 3, 3, 3],
"Excel": [4, 5, 5, 4],
"Data Visualization": [5, 5, 3, 3],
"Power Bi/ Tableau": [4, 5, 3, 4],
"Version Control Git": [4, 4, 3, 3],
"ETL/Data pipeline": [3, 2, 1, 2],
"Communication": [4, 4, 5, 3],
"Project Management": [5, 5, 5, 3],
"Cloud Computing": [4, 4, 2, 2]
}
df_skills = pd.DataFrame(skills_data)
df_skills.set_index("Name", inplace=True)
df_skills| Python | SQL | Machine Learning | PySpark | Excel | Data Visualization | Power Bi/ Tableau | Version Control Git | ETL/Data pipeline | Communication | Project Management | Cloud Computing | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Name | ||||||||||||
| Binderiya | 4 | 4 | 2 | 3 | 4 | 5 | 4 | 4 | 3 | 4 | 5 | 4 |
| Pranjul | 4 | 4 | 3 | 3 | 5 | 5 | 5 | 4 | 2 | 4 | 5 | 4 |
| Pratham | 5 | 5 | 2 | 3 | 5 | 3 | 3 | 3 | 1 | 5 | 5 | 2 |
| Panyang | 3 | 4 | 2 | 3 | 4 | 3 | 4 | 3 | 2 | 3 | 3 | 2 |
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(7, 4))
sns.heatmap(df_skills, annot=True, cmap="YlGnBu", linewidths=0.5)
plt.title("Team Skill Levels Heatmap")
plt.show()import plotly.graph_objects as go
from IPython.display import IFrame
fig = go.Figure()
for name in df_skills.index:
values = df_skills.loc[name].tolist()
values += values[:1] # close the loop
fig.add_trace(go.Scatterpolar(
r=values,
theta=df_skills.columns.tolist() + [df_skills.columns[0]],
fill='toself',
name=name
))
fig.update_layout(
polar=dict(radialaxis=dict(visible=True, range=[0, 5])),
showlegend=True,
title='Team Skills Radar Chart'
)
fig.write_html("figures/skills_radar_chart.html")
IFrame(src="figures/skills_radar_chart.html", width='100%', height=500)
fig.show()